What Determines Creditor Recovery Rates?

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The 2007-09 financial crisis illustrated the importance of healthy banks for the overall stability of the financial system and economy. Because banking is inherently risky, the health of banks depends importantly on their ability to manage risk and the associated exposure to losses. The crisis revealed that risk management at banks and other financial institutions had shortcomings. As a result, the riskiness of their loans and other investments resulted in large losses that arguably contributed to the severity of the recession.

An important component of a strong risk management system is a bank's ability to assess the potential losses on its investments. One factor that determines the extent of losses is the recovery rate on loans and bonds that are in default. The recovery rate measures the extent to which the creditor recovers the principal and accrued interest due on a defaulted debt. While financial companies, their regulators, and researchers commonly assume that the recovery rate is constant, in practice, actual recovery rates vary significantly. Moreover, recovery rates are systematically related to default rates. For example, recovery rates on corporate bonds are inversely related to the aggregate corporate default rate. As a result, assuming constant recovery rates can lead to an incorrect assessment of potential losses, which in turn, would reduce the effectiveness of risk management programs.

One reason why recovery and default rates may be inversely related is that they are both likely to be strongly influenced by the economy. For example, the same adverse economic conditions that cause defaults to rise--such as a recession--can cause recoveries to fall. Drawing on more than 30 years of recovery data on defaulted debt instruments, this article shows that the state of the economy does indeed help determine creditor recovery rates. Industry distress also drives recovery rates, and evidence suggests that industry distress can be triggered by an overall weak economy.

Section I examines why the recovery rate is an important input to credit risk models. Section II analyzes recovery rates on U.S. corporate debt securities. It shows that recoveries vary considerably across time, sectors, seniority, and security type of the defaulted debt instrument. The variation in the recovery rate across time is also related to the aggregate default rate and to the business cycle. Section III examines in detail the different potential factors that explain recoveries, including bond market conditions, the macroeconomy, industry distress, and their interrelationships.

I. THE RECOVERY RATE

The goal of risk management is to reduce the risk of large losses and to increase a financial firm's resilience to large losses. One key assumption in risk management is how the recovery rate is determined. This assumption is important because additional risk is introduced when the recovery rate is not constant. Weaknesses in modeling this risk may cause common measures of credit risk to be understated.

The recovery rate in credit risk

Credit risk is the dominant source of risk for banks (Pesaran, Schuermann, Treutler, and Weiner). Credit risk is the risk of changes in value from unexpected changes in credit quality (Duffle and Singleton). (1) Unexpected changes in credit quality can come from changes to the likelihood of default, the exposure at default, and the loss given default (where loss given default is 1 minus the recovery rate). Credit risk therefore comprises both default risk and recovery risk, where recovery risk is the chance of recovering less than the full amount of principal and accrued interest due, given a default event. (2) Recovery is uncertain and often less than the full amount due, meaning that the recovery rate varies between zero and 100 percent.

A common assumption in analyzing credit risk, however, is that the recovery rate is known with certainty, so that the analysis focuses on modeling the likelihood of default. For example, the recovery rate is often a constant based on historical averages, such as between 40 percent and 50 percent on debt issued by U.S. corporate borrowers and 25 percent on debt issued by sovereign borrowers (Das and Hanouna). (3) Essentially, certain recovery means that recovery risk is assumed away. The expected default loss rate on a particular credit portfolio is then calculated as the default probability multiplied by a constant loss given default.

For example, Giesecke, Longstaff, Schaefer, and Strebulaev focus on explaining default rates over a 150-year period, applying a long-run average loss rate of 50 percent. Realized corporate bond defaults are shown to duster at various times over the historical period they examine, including the railroad crisis of 1873-75, the banking panics of the late 1800s, and the Great Depression. Default rates are modeled by a variety of factors. Macroeconomic factors such as GDP growth, stock returns, and stock return volatility are strong predictors of default rates.

Even when studies allow the recovery rate to vary randomly, they commonly assume it is not systematically related to factors like the default rate or the business cycle. (4) This assumption considerably simplifies the portfolio loss analysis because the correlation between defaults and recoveries does not have to be modeled. Moreover, researchers disagree about the need to model a systematic recovery in practice. For example, some have argued that since the recovery rate represents the outcome of a bargaining process between the debtor and the creditor, it is reasonable to assume that it is unsystematic (Longstaff and Schwartz).

Why recovery risk matters

While assuming a 40 percent to 50 percent certain recovery rate may be a good approximation for average losses, it can, nonetheless, bias estimates of credit risk. Specifically, when the recovery rate and the default probability are incorrectly assumed to be uncorrelated, key measures of credit risk can be misleadingly low. What makes the recovery rate and the default probability inversely related? An inverse relation between the recovery rate and default can arise from common dependence on an aggregate factor such as the business cycle. That is, economic downturns cause defaults to rise at the same time they push down the recovery rate. Intuitively, creditor recoveries will depend on the value of the debt collateral. But the collateral, and the economic worth of the defaulting firm's assets more generally, are expected to fall during a recession due to reduced business opportunities. (5)

To understand how key measures of credit risk can be underestimated, it is important to first provide an intuition for measuring credit risk. Suppose, for example, that a bank's credit portfolio consists of 100 identical $1 loans to U.S. businesses with a one-year maturity. If the likelihood of default and the recovery rate both were certain (say 50 percent each), the bank's risk manager would know for sure that the one-year ahead loss would be $25. Thus, risk is removed in this unrealistic example because the credit loss will always be 25 percent. But in practice, losses will be distributed over the range of zero to $100. So, while the likelihood that the loss will be less than or equal to $100 is 1, the likelihood of any particular loss value is not 1.

The risk of loss can be measured in a variety of ways. One common measure is known as Value at Risk (VaR). The VaR can be thought of as measuring the risk of a large loss. A large loss can be thought of as the level of loss that has a pre-specified low likelihood, say 1 percent, of being exceeded in practice. For example, in the situation described above, the bank might estimate that the likelihood of a loss of $90 or more might be 1 percent (see Appendix 1 for details). In some applications, such as the Federal Reserve's recent stress tests of the largest banking organizations, the likelihood of a large loss is conditioned on adverse macroeconomic outcomes. The accompanying Box describes how recovery rates were applied in these stress tests.

Altman, Brady, Resti, and Sironi simulate losses on a representative credit portfolio comparing the case when default probabilities and recoveries are assumed to be uncorrelated with the case where they are correlated. They find that potential large losses are understated in the uncorrelated case by roughly 30 percent, meaning that banks may hold insufficient capital buffers to absorb large losses that could occur if defaults and recoveries are in fact correlated. Similarly, Bruche and Gonzalez-Aguado show that the VaR can be 40 percent higher when default probabilities and recovery rates are assumed to commonly depend on an underlying credit cycle.

BOX
STRESS TESTING--AN EXAMPLE OF HOW RECOVERY RATES ARE USED IN
PRACTICE
To help protect the economy from future financial instability,
financial policymakers and regulators have made broad changes to
how the financial sector is monitored and regulated. One change is
to use stress testing, particularly of the largest banking
organizations. The purpose of stress tests is to understand how
adverse macroeconomic conditions would affect the losses, revenues,
and capital levels of these companies, individually and as a group,
and then to require specific actions to ensure that the companies
could remain viable should such adverse conditions occur.
Robust stress test scenarios simulate the most likely of the
unlikely bad economic outcomes over a specified future horizon. A
banking organization "passes" the stress test if it has sufficient
capital to absorb losses and maintain lending under bad outcomes
throughout the planning horizon. The Federal Reserve recently
conducted stress tests on the largest 19 bank holding companies and
determined that most banks would be able to maintain capital above
minimum levels during a severe economic crisis characterized by
unemployment rising to 13 percent, house prices falling by 21
percent, and stock prices plunging by 50 percent. (19)
Recovery rates were a key input in the estimation of potential
losses in the stress test. A typical loss on a loan portfolio was
projected by multiplying the exposure at default by the probability
of default and by the loss given default (Appendix B in Board of
Governors of the Federal Reserve System). …

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